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GPT-4.1 Mini vs Qwen3.5-Flash

Qwen3.5-Flash costs 6.2x less per output token than GPT-4.1 Mini ($0.26 vs $1.60 per million). Updated October 22, 2026; 46 benchmarks have a result for both.

Replace GPT-4.1 Mini Replace Qwen3.5-Flash
GPT-4.1 MiniOpenAI
Coding
No data
Overall
31.9
Output $/M
$1.60
0
Qwen3.5-FlashQwen
Coding
41.1
Overall
50.1
Output $/M
$0.26
46
vs boards won of 46 shared

GPT-4.1 Mini or Qwen3.5-Flash: which to pick

Only Qwen3.5-Flash has a coding score, 41.1 on the Vibe Coding Index; GPT-4.1 Mini has none, so the two cannot be ranked on coding.

Pick GPT-4.1 Mini for a larger context window (1.05M against 1M tokens).

Model AGPT-4.1 MiniOpenAINot benchmarkedNo coding benchmark$1.818 per solved taskModel BQwen3.5-FlashQwen41.1/1002 independent benchmarks$0.40 per solved task
Shared results
46
GPT-4.1 Mini only
40
Qwen3.5-Flash only
9
Like-for-like categories
6 of 9

Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.

Which one for your work

Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.

At a glance

Coding index
GPT-4.1 Mini not benchmarked, Qwen3.5-Flash 41.1 from 2 benchmarks.
Widest category
Math, 23.2 points apart on average over 5 shared benchmarks.
Widest single gap
AIME: GPT-4.1 Mini 49.4% against Qwen3.5-Flash 92.5%, 43.1 points apart on a 0 to 100 scale.
Output price
GPT-4.1 Mini $1.60 against Qwen3.5-Flash $0.26 per million output tokens, 6.2x apart.
Context window
GPT-4.1 Mini takes 1.05M tokens, Qwen3.5-Flash 1M.

Benchmark by benchmark

First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.

Boards won, where both have a result
CategoryGPT-4.1 MiniBarsQwen3.5-FlashTies
Coding03
Intelligence01
Reasoning08
Math05
Knowledge010
General019
All shared boards046
CodingLike-for-likeSix of these boards feed the Vibe Coding Index03
Coding benchmarks for GPT-4.1 Mini and Qwen3.5-Flash
BenchmarkGPT-4.1 MiniQwen3.5-FlashLeader
Aider polyglot32.4%Not measuredOnly GPT-4.1 Mini measured
SWE-bench Verified23.9%Not measuredOnly GPT-4.1 Mini measured
LMArena WebDevNot measured1244Only Qwen3.5-Flash measured
AA Coding Index20.2Not measuredOnly GPT-4.1 Mini measured
LiveCodeBench (Vals AI)58.2%83.3%Qwen3.5-Flash
SWE-bench Verified (Vals AI)Not measured64.4%Only Qwen3.5-Flash measured
LiveCodeBench (Artificial Analysis run)48.3%Not measuredOnly GPT-4.1 Mini measured
Terminal-Bench Hard (Artificial Analysis run)7.6%Not measuredOnly GPT-4.1 Mini measured
WeirdML37.6%Not measuredOnly GPT-4.1 Mini measured
ALE-BenchNot measured265.9Only Qwen3.5-Flash measured
LMArena Text: coding13671412Qwen3.5-Flash
LMArena Text: software and IT13601416Qwen3.5-Flash
LMArena WebDev: ReactNot measured1229Only Qwen3.5-Flash measured
LMArena WebDev: HTMLNot measured1291Only Qwen3.5-Flash measured
Neither model has a result on the other 25 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for GPT-4.1 Mini and Qwen3.5-Flash
BenchmarkGPT-4.1 MiniQwen3.5-FlashLeader
AA Intelligence Index10.2Not measuredOnly GPT-4.1 Mini measured
Epoch Capabilities Index134.9143.5Qwen3.5-Flash
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
Agentic work benchmarks for GPT-4.1 Mini and Qwen3.5-Flash
BenchmarkGPT-4.1 MiniQwen3.5-FlashLeader
τ²-Bench Telecom52.9%Not measuredOnly GPT-4.1 Mini measured
Berkeley Function Calling Leaderboard (BFCL)50.4%Not measuredOnly GPT-4.1 Mini measured
Neither model has a result on the other 39 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index08
Reasoning benchmarks for GPT-4.1 Mini and Qwen3.5-Flash
BenchmarkGPT-4.1 MiniQwen3.5-FlashLeader
GPQA Diamond67.9%82.8%Qwen3.5-Flash
MMMU Pro (Vals AI)70.5%81.9%Qwen3.5-Flash
GPQA Diamond (Artificial Analysis run)66.4%Not measuredOnly GPT-4.1 Mini measured
Humanity's Last Exam (Artificial Analysis run)5.0%Not measuredOnly GPT-4.1 Mini measured
GPQA Diamond (Epoch AI run)65.8%82.3%Qwen3.5-Flash
ARC-AGI3.5%Not measuredOnly GPT-4.1 Mini measured
ARC-AGI-20.0%Not measuredOnly GPT-4.1 Mini measured
Chess puzzles1.0%14.0%Qwen3.5-Flash
Mystery game puzzles7.0%20.0%Qwen3.5-Flash
LMArena Text: hard prompts13491403Qwen3.5-Flash
LMArena Text: expert13381407Qwen3.5-Flash
LMArena Text: hard prompts in English13621409Qwen3.5-Flash
LMArena Vision: diagrams1182Not measuredOnly GPT-4.1 Mini measured
LMArena Vision: homework1214Not measuredOnly GPT-4.1 Mini measured
Kagi LLM Benchmark48.6%Not measuredOnly GPT-4.1 Mini measured
Neither model has a result on the other 7 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index05
Math benchmarks for GPT-4.1 Mini and Qwen3.5-Flash
BenchmarkGPT-4.1 MiniQwen3.5-FlashLeader
AIME49.4%92.5%Qwen3.5-Flash
MATH 500 (Vals AI)88.0%Not measuredOnly GPT-4.1 Mini measured
MGSM (Vals AI)87.8%Not measuredOnly GPT-4.1 Mini measured
AA Math Index46.3Not measuredOnly GPT-4.1 Mini measured
AIME 2025 (Artificial Analysis run)46.3%Not measuredOnly GPT-4.1 Mini measured
OTIS Mock AIME44.7%84.4%Qwen3.5-Flash
FrontierMath, tiers 1 to 36.7%18.2%Qwen3.5-Flash
MATH level 587.3%Not measuredOnly GPT-4.1 Mini measured
LMArena Text: math13431407Qwen3.5-Flash
LMArena Text: mathematical work13471413Qwen3.5-Flash
Neither model has a result on the other 3 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index010
Knowledge benchmarks for GPT-4.1 Mini and Qwen3.5-Flash
BenchmarkGPT-4.1 MiniQwen3.5-FlashLeader
MMLU Pro77.2%84.1%Qwen3.5-Flash
LegalBench (Vals AI)78.0%84.3%Qwen3.5-Flash
CaseLaw (Vals AI)Not measured56.0%Only Qwen3.5-Flash measured
CorpFin (Vals AI)57.9%63.6%Qwen3.5-Flash
TaxEval (Vals AI)71.9%72.2%Qwen3.5-Flash
MortgageTax (Vals AI)65.5%67.4%Qwen3.5-Flash
MedQA (Vals AI)84.6%Not measuredOnly GPT-4.1 Mini measured
MedCode (Vals AI)Not measured33.0%Only Qwen3.5-Flash measured
MMLU Pro (Artificial Analysis run)78.1%Not measuredOnly GPT-4.1 Mini measured
SimpleQA Verified12.7%20.3%Qwen3.5-Flash
LMArena Text: science13281415Qwen3.5-Flash
LMArena Text: medicine and healthcare13241407Qwen3.5-Flash
LMArena Text: legal and government13431398Qwen3.5-Flash
LMArena Text: business and finance13221394Qwen3.5-Flash
Neither model has a result on the other 4 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
Web design benchmarks for GPT-4.1 Mini and Qwen3.5-Flash
BenchmarkGPT-4.1 MiniQwen3.5-FlashLeader
Design Arena: websites997Not measuredOnly GPT-4.1 Mini measured
Design Arena: UI components958Not measuredOnly GPT-4.1 Mini measured
Design Arena: game dev1070Not measuredOnly GPT-4.1 Mini measured
Design Arena: data viz1034Not measuredOnly GPT-4.1 Mini measured
Design Arena: 3D836Not measuredOnly GPT-4.1 Mini measured
Design Arena: all code categories993Not measuredOnly GPT-4.1 Mini measured
Neither model has a result on the other 3 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index019
General benchmarks for GPT-4.1 Mini and Qwen3.5-Flash
BenchmarkGPT-4.1 MiniQwen3.5-FlashLeader
SAGE (Vals AI)Not measured42.5%Only Qwen3.5-Flash measured
MedScribe (Vals AI)Not measured70.6%Only Qwen3.5-Flash measured
IFBench38.3%Not measuredOnly GPT-4.1 Mini measured
Long context reasoning (AA-LCR)44.0%Not measuredOnly GPT-4.1 Mini measured
DTBench68.8%82.9%Qwen3.5-Flash
LMCA21.1%29.1%Qwen3.5-Flash
Fiction.LiveBench44.4%Not measuredOnly GPT-4.1 Mini measured
LMArena Text13411397Qwen3.5-Flash
LMArena Text: instruction following13331374Qwen3.5-Flash
LMArena Text: creative writing12991343Qwen3.5-Flash
LMArena Vision1181Not measuredOnly GPT-4.1 Mini measured
LMArena Text: English13561404Qwen3.5-Flash
LMArena Text: non-English13181385Qwen3.5-Flash
LMArena Text: Chinese13291445Qwen3.5-Flash
LMArena Text: German13501390Qwen3.5-Flash
LMArena Text: Japanese12901369Qwen3.5-Flash
LMArena Text: Korean12981344Qwen3.5-Flash
LMArena Text: French13581413Qwen3.5-Flash
LMArena Text: Spanish13191400Qwen3.5-Flash
LMArena Text: Russian13241380Qwen3.5-Flash
LMArena Text: Polish13261376Qwen3.5-Flash
LMArena Text: multi-turn13541393Qwen3.5-Flash
LMArena Text: longer queries13441392Qwen3.5-Flash
LMArena Text: writing and language13191369Qwen3.5-Flash
LMArena Text: entertainment and media13001347Qwen3.5-Flash
LMArena Vision: English1191Not measuredOnly GPT-4.1 Mini measured
LMArena Vision: Chinese1186Not measuredOnly GPT-4.1 Mini measured
LMArena Vision: reading text1188Not measuredOnly GPT-4.1 Mini measured
LMArena Vision: humor1182Not measuredOnly GPT-4.1 Mini measured
LMArena Vision: captioning1174Not measuredOnly GPT-4.1 Mini measured
LMArena Vision: recognizing things1187Not measuredOnly GPT-4.1 Mini measured
LMArena Vision: creative writing1182Not measuredOnly GPT-4.1 Mini measured
Neither model has a result on the other 14 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GPT-4.1 Mini and Qwen3.5-Flash
BenchmarkGPT-4.1 MiniQwen3.5-FlashLeader
Neither model has a result on 5 boards here

What each workload costs

Three fixed token mixes at each model's listed price, so per-token rates become a bill.

Chat turn

1K input + 500 output tokens

GPT-4.1 Mini$0.0012Fits in one request
Qwen3.5-Flash$0.0002Fits in one request

Qwen3.5-Flash costs less

Repository review

50K input + 3K output tokens

GPT-4.1 Mini$0.025Fits in one request
Qwen3.5-Flash$0.0040Fits in one request

Qwen3.5-Flash costs less

Agent loop

200K input + 10K output tokens

GPT-4.1 Mini$0.096Fits in one request
Qwen3.5-Flash$0.016Fits in one request

Qwen3.5-Flash costs less

Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.

Specification differences

The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.

Specifications of GPT-4.1 Mini and Qwen3.5-Flash
SpecificationOpenAI GPT-4.1 MiniQwen3.5-Flash
MakerOpenAIQwen
WeightsNo public weights listedNo public weights listed
ReleasedApril 14, 2025February 25, 2026
Context window1.05M1M
Input $/Mtok$0.40$0.065
Output $/Mtok$1.60$0.26
Coding indexNot benchmarked41.1
Independent measurements behind it02
Cost per solved task$1.818$0.40
SourceOpenRouter listingOpenRouter listing

Questions

Is GPT-4.1 Mini better than Qwen3.5-Flash for coding?

Only Qwen3.5-Flash has a coding score, 41.1 on the Vibe Coding Index; GPT-4.1 Mini has none, so the two cannot be ranked on coding. Pick GPT-4.1 Mini for a larger context window (1.05M against 1M tokens).

Which is cheaper, GPT-4.1 Mini or Qwen3.5-Flash?

GPT-4.1 Mini costs $0.40 per million input tokens and $1.60 per million output tokens; Qwen3.5-Flash costs $0.065 and $0.26. Per solved SWE-bench Verified task, GPT-4.1 Mini costs $1.818 and Qwen3.5-Flash $0.40.

Which has the bigger context window, GPT-4.1 Mini or Qwen3.5-Flash?

GPT-4.1 Mini, with 1.05M tokens against 1M.

How many benchmarks is the GPT-4.1 Mini vs Qwen3.5-Flash comparison based on?

46 benchmarks have a result for both models as of October 22, 2026. GPT-4.1 Mini's coding index rests on 0 independent benchmarks and Qwen3.5-Flash's on 2. Where only one model has a result, the page says so instead of naming a winner.

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